The number of orthopedic surgeries has been growing rapidly because of the aging population as well as development of medical technologies. Joint replacements, which have extended beyond hips and knees, are being performed on younger candidates with an increasing percentage of follow-on surgical revisions.
Bone segmentation from medical imaging assists planning for these orthopedic surgeries. The segmentation of bone tissue from a three-dimensional medical image, such as computed tomography (CT) image or a magnetic resonance (MR) image, allows engineering analysis for the replacement and optimization in individualized orthopedic surgical procedures.
Despite the large volume of research, the development of efficient and cost-effective algorithms for bone segmentation is still an open field. Most previous approaches are learning-based and rely heavily on a large database of annotated training images. However, it is very expensive and time-consuming to obtain a large amount of annotated images produced by human experts. Manual segmentation is also not optimal. Depicting the boundary of a bone slice-by-slice through a CT or other volume may be very tedious.